By Vladik Kreinovich, Songsak Sriboonchitta, Van-Nam Huynh
This e-book offers contemporary study on robustness in econometrics. powerful information processing ideas – i.e., options that yield effects minimally suffering from outliers – and their functions to real-life financial and monetary events are the focus of this publication. The ebook additionally discusses purposes of extra conventional statistical ideas to econometric problems.
Econometrics is a department of economics that makes use of mathematical (especially statistical) the way to learn monetary structures, to forecast monetary and monetary dynamics, and to advance suggestions for reaching fascinating fiscal functionality. In daily facts, we frequently come upon outliers that don't mirror the long term fiscal developments, e.g., unforeseen and abrupt fluctuations. As such, it is very important improve powerful facts processing recommendations which may accommodate those fluctuations.
Read Online or Download Robustness in Econometrics PDF
Best econometrics books
This hugely profitable textual content specializes in exploring replacement concepts, mixed with a realistic emphasis, A consultant to replacement options with the emphasis at the instinct in the back of the ways and their functional reference, this new version builds at the strengths of the second one variation and brings the textual content thoroughly up–to–date.
Instruments to enhance choice making in a less than perfect global This ebook offers readers with a radical figuring out of Bayesian research that's grounded within the conception of inference and optimum selection making. modern Bayesian Econometrics and records presents readers with cutting-edge simulation equipment and versions which are used to unravel advanced real-world difficulties.
This number of unique articles-8 years within the making-shines a vibrant gentle on contemporary advances in monetary econometrics. From a survey of mathematical and statistical instruments for realizing nonlinear Markov tactics to an exploration of the time-series evolution of the risk-return tradeoff for inventory industry funding, famous students Yacine AГЇt-Sahalia and Lars Peter Hansen benchmark the present nation of information whereas participants construct a framework for its progress.
- Rational Expectations and Econometric Practice - Volume 2
- Basics of Modern Mathematical Statistics: Exercises and Solutions
- Time Series: Theory and Methods (Springer Series in Statistics)
- Statistical Analysis of Financial Data in R
- Spatial Econometrics
Additional resources for Robustness in Econometrics
Kreinovich · O. Kosheleva University of Texas at El Paso, 500 W. edu O. edu © Springer International Publishing AG 2017 V. Kreinovich et al. 1007/978-3-319-50742-2_3 51 52 S. Sriboonchitta et al. possible decisions—and then, to use these predictions to select the decision for which the corresponding prediction is the most preferable. When we have the full knowledge of the situation, the problem of selecting the best decision becomes a straightforward optimization problem. In practice, however, we rarely have the full knowledge.
In this section we also provide another practical example that corresponds to a stochastic volatility model (that is inspired by the Heston  model) and we describe backward smoothing of the resulting estimates. 2 Linear State Space Models In this section we present two explicit examples of linear state space models: (1) a Gaussian one in Sect. 1 and (2) a non-Gaussian one in Sect. 2. Moreover, we present the Kalman filter technique that solves these models. 1 Gaussian Linear State Space Models and the Kalman Filter In many situations Gaussian linear state space models are studied.
Econ Rev 31(3):245–296 3. Del Moral P (1996) Non linear filtering: interacting particle solution. Markov Process Relat Fields 2(4):555–580 4. Del Moral P, Doucet A, Jasra A (2006) Sequential Monte Carlo samplers. J R Stat Soc Ser B 68(3):411–436 5. Del Moral P, Peters GW, Vergé C (2012) An introduction to stochastic particle integration methods: with applications to risk and insurance. In: Dick J, Kuo FY, Peters GW, Sloan IH (eds) Monte Carlo and Quasi-Monte Carlo Methods. Springer Proceedings in Mathematics & Statistics.